Application Open:
Full-Time
Job Purpose:
The job purpose of an AI/NLP Engineer at MBZUAI is to leverage expertise in designing and implementing sophisticated AI models and NLP algorithms to support diverse research projects. This pivotal role entails collaborating with interdisciplinary research teams to develop cutting-edge AI and NLP solutions for complex challenges and impactful outcomes. Proficiency in fine-tuning large language models is crucial for analyzing intricate datasets, extracting valuable insights, and informing critical decision-making processes to enhance research outcomes. By contributing to the advancement of high-quality NLP techniques, this position actively promotes AI research, fosters a culture of innovation, and positions MBZUAI at the forefront of shaping the future of research and technological advancements.
Key Responsibilities:
- AI Development
- Lead the development of cutting-edge AI models and algorithms, utilizing expertise in fine-tuning large language models to design the architecture and implementation. This critical role demands a profound grasp of research objectives, strategic selection of AI technologies, and the creation of bespoke solutions that precisely align with MBZUAI’s research excellence goals.
- Research Collaboration
- Collaborate closely with faculty members, research teams, and academic departments to identify research opportunities where AI technologies, including large language models, can be effectively applied to provide innovative solutions.
- Analyze project requirements, pinpoint AI applications, and surmount challenges to optimize artificial intelligence in research endeavors.
- Project Execution
- Contribute to the design and execution of research projects. This involves working with project managers and research teams to ensure the successful application of AI technologies, from the initial design phase through to project completion.
- The AI Engineer will design, and construct AI solutions tailored to specific research requirements, continuously optimizing these solutions to improve project outcomes and foster innovation within the university’s research environment.
- Data Analysis
- Analyze and interpret complex data to provide insights and inform research outcomes. This involves using AI and machine learning techniques to analyze data, identify patterns and trends, and provide actionable insights that can guide research decisions.
- Technical Support
- Provide technical expertise and support to research teams, helping them leverage AI technologies effectively. This involves troubleshooting technical issues, providing training and guidance on AI technologies, and ensuring the smooth operation of AI systems and tools.
- Cross-functional Collaboration
- Collaborate with other departments, such as IT and Data Science, to ensure seamless integration and efficient use of AI technologies.
- Model Optimization
- Continually monitor and optimize AI models and algorithms to ensure they are delivering the best possible results. Make adjustments as necessary based on feedback and performance metrics.
- Knowledge Enhancement
- The AI Engineer will stay abreast of current trends, technologies, and methodologies in artificial intelligence, leveraging this knowledge to drive advancements in AI research and maintain the university’s position as a leader in the field.
- Other Duties
- Carry out all other duties as reasonably directed by the Line Manager, to support the department and MBZUAI’s objectives.
Academic Qualification:
- Bachelor’s degree in Computer Science, AI Machine Learning, or a related field is required.
Professional Experience:
- Minimum of 6 to 8 years’ experience with 3 years of experience in developing AI solutions, machine learning algorithms, and data analytics in a research or academic setting, including expertise in fine-tuning large language models.
- Proficiency in programming languages such as Python, R, or Java, and experience with AI frameworks like TensorFlow, PyTorch, or sci-kit-learn.
- Strong understanding of AI concepts, algorithms, and methodologies, with a focus on practical application in research projects.
- Experience in data preprocessing, feature engineering, model training, validation, and deployment in AI projects.
Preferred:
- Masters in AI, Machine Learning, or a relevant discipline is preferred.
- Experience working in research-intensive environments or world-renowned research-based universities known for their AI expertise and innovation.
- Ability to work collaboratively in multidisciplinary teams, communicate complex technical concepts effectively, and adapt to evolving research requirements.
